Many problems arising in image processing and signal recovery with multi -regularization and constraints can be formulated as minimization of a sum of three convex separable functions. Typically, the objective function involves a smooth function with Lipschitz continuous gradient, a linear composite nonsmooth function and a nonsmooth function. In this talk, we aim to propose a primal-dual fixed point (PDFP) scheme to solve the above class of problems. The proposed algorithm is a symmetric and fully splitting scheme, only involving explicit gradient, linear transform and the proximity operators which may have closed-form solution. The convergence of the algorithm is established and some numerical examples are performed to show its efficiency. This is a joint work with Peijun Chen and Xiaoqun Zhang from Shanghai Jiao Tong University.